A Survey of Time-Series Prediction for Digitally Enabled Maintenance of Electrical Grids

被引：8
|
作者：
Mirshekali, Hamid [1 ]
Santos, Athila Q. [1 ]
Shaker, Hamid Reza [1 ]
机构：
[1] Univ Southern Denmark, SDU Ctr Energy Informat, DK-5230 Odense, Denmark
来源：
ENERGIES | 2023年 / 16卷 / 17期
关键词：
time-series forecasting; digitally enabled maintenance; electrical grid; artificial intelligence; SUPPORT VECTOR MACHINE; FAULT LOCATION; NEURAL-NETWORK; PROACTIVE MAINTENANCE; POWER TRANSMISSION; ENERGY-RESOURCES; DECISION-MAKING; VOLTAGE; FLASHOVER; SYSTEM;
D O I：
10.3390/en16176332
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
The maintenance of electrical grids is crucial for improving their reliability, performance, and cost-effectiveness. It involves employing various strategies to ensure smooth operation and address potential issues. With the advancement of digital technologies, utilizing time-series prediction has emerged as a valuable approach to enhance maintenance practices in electrical systems. The utilization of various recorded data from electrical grid components plays a crucial role in digitally enabled maintenance. However, the comprehensive exploration of time-series data prediction for maintenance is still lacking. This review paper extensively explores different time series that can be utilized to support maintenance efforts in electrical grids with regard to different maintenance strategies and grid components. The digitization of the electrical grids has enabled the collection of diverse time-series data from various network components. In this context, the paper provides an overview of how these time-series and historical-fault data can be utilized for maintenance purposes in electrical grids. Various maintenance levels and time series used for maintenance purposes in different components of the electrical grid are presented.
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页数：29
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